Forward Deployed Engineering Manager Iii, Genai, Google Cloud

Google Google · Big Tech · São Paulo, State of São Paulo, Brazil

Manager of a Generative AI Forward Deployed Engineering (FDE) team, leading AI/ML engineers to deploy bespoke agentic solutions within customer environments. Responsibilities include technical mentorship, hiring, identifying skill gaps, and collaborating with product/engineering. Focus on enterprise-grade AI maturity, leveraging Google Cloud's AI portfolio and Vertex AI platform.

What you'd actually do

  1. Serve as the technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
  2. Partner with sales and tech leadership to define requirements for high-value opportunities, deploying specialized experts (MLOps, GenMedia, or Agentic systems) to key accounts.
  3. Lead technical hiring for Forward Deployed Engineer (FDE), evaluating AI/ML expertise, systems engineering, and coding skills to build an exceptional engineering team.
  4. Identify skill gaps in emerging tech (model context protocol (MCP), tool-calling, and foundation models), ensuring the team maintains subject matter expertise in an evolving AI stack.
  5. Collaborate with product and engineering to resolve blockers and translate field insights into roadmaps while building internal tools to drive organizational efficiency.

Skills

Required

  • Python or similar coding languages
  • Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or retrieval-augmented generation (RAG) systems.

Nice to have

  • Master’s degree or PhD in AI, Computer Science, or a related technical field.
  • Experience designing end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.
  • Experience designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
  • Experience architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
  • Experience performing discovery interviews to identify business problems and translate complex hardware/AI constraints for C-suites and technical teams.

What the JD emphasized

  • deploying bespoke agentic solutions directly within customer environments
  • resolve production-level obstacles, including data readiness issues, integration complexities, and state-management issues that hinder AI from achieving enterprise-grade maturity
  • Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or retrieval-augmented generation (RAG) systems.
  • Experience designing end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.

Other signals

  • leading AI/ML engineers
  • deploying bespoke agentic solutions
  • customer environments
  • production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management issues
  • enterprise-grade maturity
  • Vertex AI platform
  • DeepMind's engineering and research minds
  • code standards
  • architectural best practices
  • high-value opportunities
  • specialized experts (MLOps, GenMedia, or Agentic systems)
  • technical hiring
  • AI/ML expertise
  • systems engineering
  • coding skills
  • skill gaps in emerging tech
  • model context protocol (MCP)
  • tool-calling
  • foundation models
  • subject matter expertise
  • evolving AI stack
  • product and engineering collaboration
  • field insights into roadmaps
  • building internal tools
  • organizational efficiency
  • Python
  • AI tools
  • multi-agent workflows
  • retrieval-augmented generation (RAG) systems
  • end-to-end secure, observable multi-agent systems
  • complex design patterns (e.g., ReAct, self-reflection)
  • state management
  • tool-calling protocols
  • intuitive interfaces for complex AI and agentic systems
  • context engineering
  • transparency
  • explainability
  • user trust
  • architecting AI solutions
  • complex infrastructures
  • data sovereignty
  • secure governance
  • discovery interviews
  • business problems
  • complex hardware/AI constraints
  • C-suites and technical teams